TY - GEN
T1 - Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation
AU - Pan, Jiazhen
AU - Rueckert, Daniel
AU - Küstner, Thomas
AU - Hammernik, Kerstin
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Cardiac motion estimation plays an essential role in motion-compensated cardiac Magnetic Resonance (MR) image reconstruction. In this work, we propose a robust and lightweight self-supervised deep learning registration framework, termed MRAFT, to estimate non-rigid cardiac motion. The proposed framework combines an efficient architecture with a novel degradation-restoration (DR) loss term, and an enhancement mask derived from a pre-trained segmentation network. This framework enables the prediction of both small and large cardiac motion more precisely, and allows us to handle through-plane motion in a 2D registration setting via the DR loss. The quantitative and qualitative experiments on a retrospective cohort of 42 in-house acquired 2D cardiac CINE MRIs indicate that the proposed method outperforms the competing approaches substantially, with more than 25% reduction in residual photometric error, and up to 100 × faster inference speed compared to conventional methods.
AB - Cardiac motion estimation plays an essential role in motion-compensated cardiac Magnetic Resonance (MR) image reconstruction. In this work, we propose a robust and lightweight self-supervised deep learning registration framework, termed MRAFT, to estimate non-rigid cardiac motion. The proposed framework combines an efficient architecture with a novel degradation-restoration (DR) loss term, and an enhancement mask derived from a pre-trained segmentation network. This framework enables the prediction of both small and large cardiac motion more precisely, and allows us to handle through-plane motion in a 2D registration setting via the DR loss. The quantitative and qualitative experiments on a retrospective cohort of 42 in-house acquired 2D cardiac CINE MRIs indicate that the proposed method outperforms the competing approaches substantially, with more than 25% reduction in residual photometric error, and up to 100 × faster inference speed compared to conventional methods.
UR - http://www.scopus.com/inward/record.url?scp=85116916905&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-88552-6_2
DO - 10.1007/978-3-030-88552-6_2
M3 - Conference contribution
AN - SCOPUS:85116916905
SN - 9783030885519
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 14
EP - 24
BT - Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Haq, Nandinee
A2 - Johnson, Patricia
A2 - Maier, Andreas
A2 - Würfl, Tobias
A2 - Yoo, Jaejun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021 held in Conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 1 October 2021 through 1 October 2021
ER -